실시간 수치예보 국제 워크숍
2017 KIAPS International Workshop On Real-Time NWP Forecast System The international workshop on real-time NWP system will take place on May 31 - June 2 Jeju, South Korea. The special attention of the workshop will be facilitating the advancement of operational data assimilation system to improve real time weather prediction, such as the cutting-edge data assimilation methodologies, the impact of observation in the operation NWP system, and future directions of operational data assimilation. The data assimilation experts from various international operational centers are invited, so we hope that the workshop provides us with good opportunity to share experience of each operational center and discuss how to future improve the forecast performance. We cordially invite you to this exciting workshop. We hope to see you at warm and beautiful Jeju Island of South Korea. 2017년 KIAPS 실시간 수치예보 국제워크숍이 5월 31일부터 6월 2일까지 아름다운 섬, 제주도에서 개최됩니다. 이번 워크숍에서는 기상예측 정확도 개선에서 가장 중요한 요소 중 하나인 자료동화시스템을 주제로, 세계적인 자료동화 전문가들을 초청하여 각 국의 최점단 자료동화시스템을 소개하고 관측자료 활용과 자료동화의 발전 방향에 대해 논의하는 기회를 가지고자 합니다. 관심있으신 많은 분들을 초청하오니 5월 31일, 따뜻하고 아름다운 제주도에서 만나요!
Development of a physically based autoconversion parameterization and its application to cloud modeling
In this study, a new autoconversion process parameterizationis derived by analytically integrating the stochastic collection equation (SCE).A Lagrangian particle model is employed to obtain the collision efficiencybetween cloud droplets. The new parameterization proposed in this study isvalidated against a bin-based direct SCE solver and compared to otherautoconversion process parameterizations using a box model. The time requiredfor 10% of the initial cloud water mass to be converted into rainwater mass isemployed for the validation. The result of the new parameterization agrees wellwith that of the direct SCE solver. Moreover, the dependency of theautoconversion rate on drop number concentration in the new parameterization issimilar to that in the direct SCE solver, whereas some other autoconversionprocess parameterizations show somewhat large dependency on drop numberconcentration. In shallow warm cloud simulations using a cloud-resolving model,the new parameterization tends to yield the moderate autoconversion rate amongthe autoconversion process parameterizations, as in the box model. When cloudoptical thickness, cloud fraction, and surface precipitation amount areselected for comparison, the results of the new parameterization are generallythe closest to those of the bin microphysics scheme. The autoconversion processparameterizations that yield the small (large) autoconversion rate tend toproduce large (small) cloud optical thickness, small (large) cloud fraction,and small (large) surface precipitation amount. The new autoconversion processparameterization is expected to give good performances in weather and climateprediction.